{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Word2Vec: Distributed Representations of Words and Phrases and their Compositionality\n",
        "\n",
        "---\n",
        "\n",
        "[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/SkalskiP/vlms-zero-to-hero)\n",
        "[![arXiv](https://img.shields.io/badge/arXiv-1310.4546-b31b1b.svg)](https://arxiv.org/abs/1310.4546)\n",
        "\n",
        "![word2vec-cheat-sheet](https://github.com/user-attachments/assets/aedfbbe9-c645-4818-9c9b-c9f1ba04d522)\n",
        "\n",
        "This notebook introduces Word2Vec, a powerful method for understanding the relationships between words by learning their \"distributed representations.\" Originally proposed by Mikolov et al. in their influential paper \"Distributed Representations of Words and Phrases and Their Compositionality\", Word2Vec has become a cornerstone of natural language processing (NLP). By representing words as vectors in a high-dimensional space, Word2Vec captures both semantic (meaning-based) and syntactic (grammar-based) relationships, enabling applications like machine translation, sentiment analysis, and text similarity.\n",
        "\n",
        "In this notebook, we’ll walk through every step of building and training the Word2Vec model using the Skip-Gram architecture. We'll start by preparing the dataset, learning how to handle common issues like overly frequent words, and explore how to create training samples. Using negative sampling—a key optimization trick introduced in the original paper—we'll efficiently train our model on large text data. Finally, we’ll evaluate the learned word vectors by finding similar words and visualizing them in 2D with t-SNE. Whether you’re new to NLP or looking for a practical introduction to Word2Vec, this notebook offers a hands-on way to understand one of the most important ideas in NLP.\n",
        "\n"
      ],
      "metadata": {
        "id": "HOPvf1VlZi4k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Dependencies"
      ],
      "metadata": {
        "id": "O2W1i4RFabDp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q numpy requests tqdm torch scikit-learn matplotlib seaborn tabulate"
      ],
      "metadata": {
        "id": "HOUEFSssaXGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ensure Reproducibility with Random Seeds\n",
        "\n",
        "Many of the algorithms used in this notebook, such as sub-sampling, negative sampling, and model initialization, rely on randomness to function. While this randomness can improve efficiency and generalization, it also means that running the same code multiple times may yield slightly different results.\n",
        "\n",
        "To ensure consistent and reproducible results across different runs of the notebook, we set random seeds for all random processes used in the code. This includes Python’s random module, NumPy, and PyTorch. By doing so, we guarantee that operations like sampling, model initialization, and training yield the same results every time the notebook is executed."
      ],
      "metadata": {
        "id": "KN1pBk0PyYMY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "SEED = 42\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    torch.cuda.manual_seed(SEED)\n",
        "    torch.cuda.manual_seed_all(SEED)"
      ],
      "metadata": {
        "id": "S-5eGpS3yjwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configure Runtime for GPU Acceleration\n",
        "\n",
        "Let's make sure that we have access to GPU. We can use `nvidia-smi` command to do that. In case of any problems navigate to `Edit` -> `Notebook settings` -> `Hardware accelerator`, set it to `T4 GPU`, and then click `Save`."
      ],
      "metadata": {
        "id": "HosmopFamuoS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0Oam2UXmwP5",
        "outputId": "693a3029-b21b-468a-fa97-e81c2f74295d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Jan 23 00:31:39 2025       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   58C    P8               9W /  70W |      3MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and Prepare Dataset\n",
        "\n",
        "This section downloads the text8 dataset, a pre-processed collection of Wikipedia text commonly used for language modeling. The text8 dataset is already cleaned and formatted: it contains only lowercase alphabetic characters, with punctuation, numbers, and case distinctions removed. The dataset is tokenized into words, making it ready for vocabulary construction and subsequent preprocessing steps."
      ],
      "metadata": {
        "id": "4arigSitoPnc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import requests\n",
        "import zipfile\n",
        "\n",
        "URL = \"http://mattmahoney.net/dc/text8.zip\"\n",
        "FILENAME = \"text8.zip\"\n",
        "\n",
        "if not os.path.isfile(FILENAME):\n",
        "    response = requests.get(URL, stream=True)\n",
        "    with open(FILENAME, 'wb') as file_obj:\n",
        "        for chunk in response.iter_content(chunk_size=1024):\n",
        "            if chunk:\n",
        "                file_obj.write(chunk)\n",
        "\n",
        "if not os.path.isfile(\"text8\"):\n",
        "    with zipfile.ZipFile(FILENAME, 'r') as zipped_file:\n",
        "        zipped_file.extractall(\".\")\n",
        "\n",
        "def load_text_file():\n",
        "    with open(\"text8\", \"r\", encoding=\"utf-8\") as file_obj:\n",
        "        text_data = file_obj.read()\n",
        "    return text_data.strip().split()\n",
        "\n",
        "words = load_text_file()\n",
        "print(words[:20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GE2HFxz9ohvd",
        "outputId": "b302dac1-234a-43e5-9153-ef764e79a71f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of words in the text8 dataset: {len(words)}\")\n",
        "print(f\"Number of unique words in the text8 dataset: {len(set(words))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ON3uBUvPz4fh",
        "outputId": "d5acca2a-5cce-4733-a778-fd5aee994634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in the text8 dataset: 17005207\n",
            "Number of unique words in the text8 dataset: 253854\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build Vocabulary with Most Frequent Tokens\n",
        "\n",
        "This section constructs a vocabulary by retaining only words that appear at least a specified number of times. Words that do not meet this frequency threshold are discarded entirely, ensuring that the vocabulary focuses on the most informative and frequently used words. This approach reduces noise and memory consumption while aligning with the Word2Vec methodology.\n"
      ],
      "metadata": {
        "id": "MTKiQ78UrJWv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "\n",
        "def build_vocabulary(words, min_frequency):\n",
        "    word_counter = Counter(words)\n",
        "    mapping = {}\n",
        "    for index, (word, count) in enumerate(word_counter.most_common()):\n",
        "        if count < min_frequency:\n",
        "            break\n",
        "        mapping[word] = index\n",
        "    return mapping"
      ],
      "metadata": {
        "id": "_Kt05pMEJzuf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MIN_FREQUENCY = 10\n",
        "\n",
        "word_to_index = build_vocabulary(words, min_frequency=MIN_FREQUENCY)\n",
        "index_to_word = {val: key for key, val in word_to_index.items()}\n",
        "vocabluary_size = len(word_to_index)"
      ],
      "metadata": {
        "id": "bNqWJI9e2uWv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = [w for w in words if w in word_to_index]\n",
        "print(f\"Number of words in the text8 dataset: {len(words)}\")\n",
        "print(f\"Number of unique words in the text8 dataset: {len(set(words))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86nMifG_MCDc",
        "outputId": "dc061280-fd48-4a86-eece-140717da4744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in the text8 dataset: 16561031\n",
            "Number of unique words in the text8 dataset: 47134\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apply Sub-sampling to Reduce Frequent Words\n",
        "\n",
        "To mitigate the dominance of frequent tokens, a sub-sampling technique is applied. Tokens that appear excessively often are probabilistically downsampled, ensuring a balanced dataset and enhancing the efficiency of embedding learning.\n",
        "\n",
        "High-frequency tokens (e.g., \"the\", \"and\") have higher frequencies and therefore lower probabilities, meaning they are more likely to be skipped. Low-frequency tokens have higher probabilities and are more likely to be included."
      ],
      "metadata": {
        "id": "pEGmeN3gE64e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def subsample_words(words, threshold):\n",
        "    word_counter = Counter(words)\n",
        "    total = len(words)\n",
        "\n",
        "    def should_discard(word):\n",
        "        frequency = word_counter[word] / total\n",
        "        if frequency > threshold:\n",
        "            p = 1 - np.sqrt(threshold / frequency)\n",
        "            return random.random() < p\n",
        "        return False\n",
        "\n",
        "    return [word for word in words if not should_discard(word)]"
      ],
      "metadata": {
        "id": "Yvp7PFyp42Uk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "THRESHOLD_FREQUENCY = 1e-5\n",
        "\n",
        "subsampled_words = subsample_words(words, threshold=THRESHOLD_FREQUENCY)\n",
        "print(\"Original number of words:\", len(words))\n",
        "print(\"Number of words after sub-sampling:\", len(subsampled_words))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aucpp0irrRLE",
        "outputId": "fa15d8dd-f5a5-45ba-8ca5-ecaa65e473f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original number of words: 16561031\n",
            "Number of words after sub-sampling: 4496739\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(words[:20])\n",
        "print(subsampled_words[:20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcAZgAwq8ITj",
        "outputId": "3e3e8128-49be-4129-ad41-8992c7a3a0a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english']\n",
            "['anarchism', 'abuse', 'radicals', 'diggers', 'revolution', 'sans', 'revolution', 'pejorative', 'way', 'violent', 'up', 'label', 'defined', 'anarchists', 'anarchism', 'archons', 'ruler', 'chief', 'anarchism', 'rulers']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sub-sampling Analysis\n",
        "\n",
        "After applying sub-sampling to reduce the dominance of high-frequency words, it's helpful\n",
        "to compare how many times each word appears before and after sub-sampling. The snippet\n",
        "below displays the first 20 words (sorted by their original frequency), along with their\n",
        "original and subsampled counts.\n",
        "\n",
        "This provides a clear demonstration of how sub-sampling removes excessively frequent words\n",
        "while retaining less common (but potentially more informative) words.\n"
      ],
      "metadata": {
        "id": "kJ_b1AGMhu0X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "from tabulate import tabulate\n",
        "\n",
        "counts = Counter(words)\n",
        "subsampled_counts = Counter(subsampled_words)\n",
        "\n",
        "# we'll focus on the first 20 words in the dataset, sorted by original frequency (descending).\n",
        "sample_words = sorted(set(words[:20]), key=lambda w: counts[w], reverse=True)\n",
        "\n",
        "table_data = []\n",
        "for w in sample_words:\n",
        "    original_count = counts[w]\n",
        "    after_count = subsampled_counts.get(w, 0)\n",
        "    table_data.append([w, original_count, after_count])\n",
        "\n",
        "print(tabulate(table_data, headers=[\"Word\", \"Original Count\", \"Subsampled Count\"], tablefmt=\"simple\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mteiUA79JXS4",
        "outputId": "91a3a20a-6513-4633-d855-566fb12077d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word          Original Count    Subsampled Count\n",
            "----------  ----------------  ------------------\n",
            "the                  1061396               13047\n",
            "of                    593677                9910\n",
            "a                     325873                7249\n",
            "as                    131815                4610\n",
            "first                  28810                2208\n",
            "used                   22737                1944\n",
            "english                11868                1346\n",
            "early                  10172                1321\n",
            "including               9633                1162\n",
            "against                 8432                1175\n",
            "term                    7219                1070\n",
            "class                   3412                 714\n",
            "working                 2271                 615\n",
            "originated               572                 281\n",
            "abuse                    563                 304\n",
            "anarchism                303                 231\n",
            "radicals                 116                 116\n",
            "diggers                   25                  25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Negative Sampling Distribution\n",
        "\n",
        "Negative sampling is a technique introduced in the Word2Vec paper to make training embeddings computationally efficient and effective. Instead of computing the gradients for all words in the vocabulary (which can be computationally expensive, especially for large vocabularies), negative sampling trains the model by updating weights for only a subset of words—specifically, a small number of \"negative\" (incorrect) samples for each positive (correct) context pair.\n",
        "\n",
        "To implement negative sampling, a frequency-adjusted distribution is used. Word frequencies are raised to the power of 3/4, a \"magic number\" introduced in the Word2Vec paper. This adjustment downweights very common words and upweights rare ones, achieving a balance that ensures more robust embeddings by sampling negatives in a meaningful way."
      ],
      "metadata": {
        "id": "ZAogA-8dGtYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_negative_sampling_distribution(indexed_words):\n",
        "    counts = np.bincount(indexed_words)\n",
        "    probablility = counts / counts.sum()\n",
        "    probablility_75 = probablility**0.75\n",
        "    return probablility_75 / probablility_75.sum()"
      ],
      "metadata": {
        "id": "GH456SP3wKBN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Custom Dataset for Skip-Gram Training\n",
        "\n",
        "A PyTorch dataset is implemented to generate training samples for the Skip-Gram model. For each target word, context words within a dynamic window and negative samples are retrieved to train the embeddings efficiently."
      ],
      "metadata": {
        "id": "PzBm6KbKHI3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_target(words, index, max_window_size=5):\n",
        "    window_size = random.randint(1, max_window_size)\n",
        "    start_position = max(0, index - window_size)\n",
        "    end_position = min(index + window_size + 1, len(words))\n",
        "    return words[start_position:index] + words[index + 1:end_position]"
      ],
      "metadata": {
        "id": "wCcRlq2ZJpiR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_target([i for i in range(20)], 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7EQhXpZJr-i",
        "outputId": "b597fd64-0e7d-4837-8b99-b566871176d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 4, 6, 7]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_target(subsampled_words[:20], 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPZRDgPNJuop",
        "outputId": "f2292d4f-845b-452a-dddc-8b3b3c43b808"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['revolution', 'revolution']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "\n",
        "class WordToVecDataset(Dataset):\n",
        "    def __init__(self, indexed_words, window_size=4):\n",
        "        self.indexed_words = indexed_words\n",
        "        self.window_size = window_size\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.indexed_words)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        center_word = self.indexed_words[index]\n",
        "        context_words = get_target(self.indexed_words, index, self.window_size)\n",
        "        return center_word, context_words"
      ],
      "metadata": {
        "id": "dt2cynm9G3Q5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = WordToVecDataset(\n",
        "    indexed_words=[i for i in range(20)],\n",
        "    window_size=4\n",
        ")\n",
        "\n",
        "center, context = dataset[0]\n",
        "print(center, context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDleRBIy4LxW",
        "outputId": "84046955-abca-45b4-cfaa-980ecb56ea4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 [1, 2, 3, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implement Collate Function for Efficient Batching\n",
        "\n",
        "This section provides a custom collate function to combine individual samples into efficient batches for training. It enables parallel processing during model training, significantly accelerating the embedding learning process.\n",
        "\n"
      ],
      "metadata": {
        "id": "A0vlXdqlL6tI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "\n",
        "def create_collate_fn(\n",
        "    vocabulary_size,\n",
        "    negative_sampling_distribution,\n",
        "    number_of_negative_samples\n",
        "):\n",
        "    negative_distribution_tensor = torch.tensor(negative_sampling_distribution, dtype=torch.float)\n",
        "\n",
        "    def collate_function(batch):\n",
        "        all_center_words = []\n",
        "        all_context_words = []\n",
        "\n",
        "        # flatten out all center-context pairs\n",
        "        for center_word, context_word_list in batch:\n",
        "            for context_word in context_word_list:\n",
        "                all_center_words.append(center_word)\n",
        "                all_context_words.append(context_word)\n",
        "\n",
        "        center_words_tensor = torch.LongTensor(all_center_words)\n",
        "        context_words_tensor = torch.LongTensor(all_context_words)\n",
        "\n",
        "        # generate negative samples for the entire batch in one shot\n",
        "        total_pairs = len(center_words_tensor)\n",
        "        negative_samples_flat = torch.multinomial(\n",
        "            negative_distribution_tensor,\n",
        "            total_pairs * number_of_negative_samples,\n",
        "            replacement=True\n",
        "        )\n",
        "\n",
        "        negative_samples_tensor = negative_samples_flat.view(total_pairs, number_of_negative_samples)\n",
        "\n",
        "        return center_words_tensor, context_words_tensor, negative_samples_tensor\n",
        "\n",
        "    return collate_function"
      ],
      "metadata": {
        "id": "gcUjF0ewHrQO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = WordToVecDataset(\n",
        "    indexed_words=[i for i in range(20)],\n",
        "    window_size=4\n",
        ")\n",
        "\n",
        "collect_fn = create_collate_fn(20, np.full(20, 1 / 20, dtype=np.float32), 5)\n",
        "\n",
        "dataloader = DataLoader(\n",
        "    dataset,\n",
        "    batch_size=4,\n",
        "    shuffle=True,\n",
        "    num_workers=2,\n",
        "    collate_fn=collect_fn,\n",
        "    drop_last=True\n",
        ")\n",
        "\n",
        "centers_tensor, contexts_tensor, negatives_tensor = next(iter(dataloader))\n",
        "print(centers_tensor)\n",
        "print(contexts_tensor)\n",
        "print(negatives_tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2o8dD5BPbu7",
        "outputId": "5f0126a4-79c7-4d5b-d0b2-589ccae4cd75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([ 8,  8,  8,  8, 14, 14, 17, 17, 17, 17, 17, 17, 19, 19])\n",
            "tensor([ 6,  7,  9, 10, 13, 15, 13, 14, 15, 16, 18, 19, 17, 18])\n",
            "tensor([[19,  5, 14,  3,  1],\n",
            "        [ 7, 11, 19, 19, 14],\n",
            "        [ 7,  3,  0,  6, 17],\n",
            "        [17, 18,  6, 10,  4],\n",
            "        [ 5,  8,  3, 16, 14],\n",
            "        [ 3,  1, 19, 15,  0],\n",
            "        [ 1,  7, 13,  4,  3],\n",
            "        [12,  4, 19,  1,  7],\n",
            "        [ 8,  9, 10,  3, 17],\n",
            "        [ 1,  0,  3,  5, 18],\n",
            "        [14,  9,  9, 14, 19],\n",
            "        [11, 18,  3,  3,  3],\n",
            "        [12,  0, 19, 12, 15],\n",
            "        [ 9, 17, 10, 18,  6]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Skip-Gram Model with Negative Sampling\n",
        "\n",
        "The Skip-Gram model with negative sampling is implemented in PyTorch. By optimizing embeddings through log-sigmoid functions for positive and negative pairs, the model learns high-quality distributed word representations that capture semantic relationships."
      ],
      "metadata": {
        "id": "ycQUojdSMxJI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class SkipGram(nn.Module):\n",
        "    def __init__(self, vocabulary_size, embedding_dimension):\n",
        "        super().__init__()\n",
        "        self.input_embeddings = nn.Embedding(vocabulary_size, embedding_dimension)\n",
        "        self.output_embeddings = nn.Embedding(vocabulary_size, embedding_dimension)\n",
        "        self.input_embeddings.weight.data.uniform_(-1, 1)\n",
        "        self.output_embeddings.weight.data.uniform_(-1, 1)\n",
        "\n",
        "    def forward(self, center_words, positive_context_words, negative_context_words):\n",
        "        center_vectors = self.input_embeddings(center_words)\n",
        "        positive_context_vectors = self.output_embeddings(positive_context_words)\n",
        "        negative_context_vectors = self.output_embeddings(negative_context_words)\n",
        "\n",
        "        # dot product for positive pairs => shape: [batch_size]\n",
        "        positive_score = torch.einsum(\"ij,ij->i\", center_vectors, positive_context_vectors)\n",
        "        # dot product for negative pairs => shape: [batch_size, K]\n",
        "        negative_score = torch.einsum(\"ijk,ik->ij\", negative_context_vectors, center_vectors)\n",
        "\n",
        "        positive_loss = F.logsigmoid(positive_score).mean()\n",
        "        negative_loss = F.logsigmoid(-negative_score).mean()\n",
        "\n",
        "        return -(positive_loss + negative_loss)"
      ],
      "metadata": {
        "id": "Zy34kv1hS1Gm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_cosine_similarity(vector_one, vector_two):\n",
        "    numerator = np.dot(vector_one, vector_two)\n",
        "    denominator = (np.linalg.norm(vector_one) * np.linalg.norm(vector_two) + 1e-9)\n",
        "    return numerator / denominator\n",
        "\n",
        "def find_similar_words(query_word, embedding_matrix, top_count=10):\n",
        "    query_index = word_to_index.get(query_word, 0)\n",
        "    query_vector = embedding_matrix[query_index]\n",
        "    similarities = []\n",
        "\n",
        "    for word, index in word_to_index.items():\n",
        "        if word == query_word:\n",
        "            continue\n",
        "        word_vector = embedding_matrix[index]\n",
        "        similarity = compute_cosine_similarity(query_vector, word_vector)\n",
        "        similarities.append((word, similarity))\n",
        "\n",
        "    similarities.sort(key=lambda x: x[1], reverse=True)\n",
        "    return similarities[:top_count]"
      ],
      "metadata": {
        "id": "BBuZPBc5bB9a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def display_similar_words(model, validation_words, word_to_index, index_to_word, top_count=5):\n",
        "    embedding_matrix = model.input_embeddings.weight.data.cpu().numpy()\n",
        "    table_data = []\n",
        "\n",
        "    for word in validation_words:\n",
        "        similar_list = find_similar_words(word, embedding_matrix, top_count)\n",
        "        similar_str = \", \".join([f\"{w} ({sim:.4f})\" for w, sim in similar_list])\n",
        "        table_data.append([word, similar_str])\n",
        "\n",
        "    print(tabulate(table_data, headers=[\"Word\", \"Most Similar Words\"], tablefmt=\"simple\"))"
      ],
      "metadata": {
        "id": "prSzVTX7jBmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from tqdm import tqdm\n",
        "from torch.utils.data import DataLoader\n",
        "import numpy as np\n",
        "import torch.nn.functional as F\n",
        "from tabulate import tabulate\n",
        "\n",
        "\n",
        "EMBEDDING_DIM = 300\n",
        "WINDOW_SIZE = 4\n",
        "NUM_NEGATIVE_SAMPLES = 5\n",
        "BATCH_SIZE = 512\n",
        "EPOCHS = 5\n",
        "LEARNING_RATE = 0.003\n",
        "\n",
        "\n",
        "indexed_words = [word_to_index.get(token, 0) for token in subsampled_words]\n",
        "negative_sampling_distribution = compute_negative_sampling_distribution(indexed_words)\n",
        "\n",
        "\n",
        "collate_function = create_collate_fn(\n",
        "    vocabulary_size=vocabluary_size,\n",
        "    negative_sampling_distribution=negative_sampling_distribution,\n",
        "    number_of_negative_samples=NUM_NEGATIVE_SAMPLES\n",
        ")\n",
        "\n",
        "dataset = WordToVecDataset(\n",
        "    indexed_words=indexed_words,\n",
        "    window_size=WINDOW_SIZE\n",
        ")\n",
        "\n",
        "dataloader = DataLoader(\n",
        "    dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        "    num_workers=2,\n",
        "    collate_fn=collate_function,\n",
        "    drop_last=True\n",
        ")\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = SkipGram(vocabluary_size, EMBEDDING_DIM).to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
        "\n",
        "loss_history = []\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))\n",
        "    progress_bar.set_description(f\"Epoch {epoch+1}/{EPOCHS}\")\n",
        "\n",
        "    for step, (centers, contexts, negs) in progress_bar:\n",
        "        centers, contexts, negs = centers.to(device), contexts.to(device), negs.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = model(centers, contexts, negs)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        current_loss_value = loss.item()\n",
        "        loss_history.append(current_loss_value)\n",
        "        progress_bar.set_postfix({\"loss\": current_loss_value})\n",
        "\n",
        "    validation_words =  [index_to_word.get(idx) for idx in random.sample(range(1, 100), 5)]\n",
        "    validation_words += [index_to_word.get(idx) for idx in random.sample(range(1000, 2000), 5)]\n",
        "    display_similar_words(model, validation_words, word_to_index, index_to_word)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "u9HXcoYXOdM7",
        "outputId": "a01b1227-083c-470f-871b-289068a893ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/5: 100%|██████████| 8782/8782 [03:17<00:00, 44.51it/s, loss=1.5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word        Most Similar Words\n",
            "----------  --------------------------------------------------------------------------------------------\n",
            "state       government (0.4750), law (0.4328), rights (0.4267), countries (0.4266), governments (0.4062)\n",
            "five        eight (0.6183), six (0.5952), seven (0.5949), four (0.5757), zero (0.5716)\n",
            "all         they (0.4464), take (0.4296), them (0.4293), to (0.4224), have (0.4196)\n",
            "many        such (0.4227), some (0.4179), personal (0.3927), not (0.3843), often (0.3825)\n",
            "states      government (0.5594), united (0.5113), republic (0.4953), democratic (0.4869), party (0.4858)\n",
            "glass       metal (0.3549), tissue (0.3183), carbon (0.3180), crystal (0.3169), ammonia (0.3128)\n",
            "materials   carbon (0.3752), atoms (0.3692), less (0.3668), chemical (0.3413), are (0.3394)\n",
            "medieval    roman (0.3833), church (0.3615), christian (0.3278), greek (0.3276), catholic (0.3258)\n",
            "equivalent  function (0.3763), properties (0.3673), set (0.3552), inverse (0.3450), numbers (0.3391)\n",
            "bring       bolshevist (0.2463), recently (0.2346), beliefs (0.2324), again (0.2320), forth (0.2269)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2/5: 100%|██████████| 8782/8782 [03:09<00:00, 46.35it/s, loss=1.25]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word          Most Similar Words\n",
            "------------  -----------------------------------------------------------------------------------------------\n",
            "been          many (0.4085), modern (0.3766), evidence (0.3552), they (0.3536), claim (0.3458)\n",
            "also          commonly (0.3037), the (0.3013), particular (0.2966), containing (0.2885), contains (0.2878)\n",
            "six           five (0.6716), three (0.6646), four (0.6542), one (0.6536), eight (0.6528)\n",
            "are           vary (0.4034), typically (0.3967), some (0.3721), consists (0.3524), populations (0.3519)\n",
            "in            major (0.3329), the (0.3284), debating (0.3256), beginning (0.3223), organized (0.3148)\n",
            "behind        shot (0.3347), drop (0.3314), up (0.3295), struck (0.3101), shock (0.3093)\n",
            "wild          animals (0.3978), cats (0.3577), birds (0.3475), habitat (0.3471), animal (0.3335)\n",
            "critics       ironically (0.3730), challenged (0.3703), fans (0.3567), accused (0.3544), argue (0.3519)\n",
            "christianity  christian (0.4695), theology (0.4552), catholic (0.4463), religious (0.4441), doctrine (0.4392)\n",
            "generation    released (0.3407), development (0.3315), atari (0.3116), pentium (0.3051), processor (0.3032)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3/5: 100%|██████████| 8782/8782 [03:10<00:00, 46.14it/s, loss=1.2]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word    Most Similar Words\n",
            "------  ---------------------------------------------------------------------------------------------\n",
            "see     article (0.3828), disambiguation (0.3255), site (0.3238), related (0.3206), history (0.3198)\n",
            "system  systems (0.4301), operating (0.3512), applications (0.3364), software (0.3359), unit (0.3244)\n",
            "known   origin (0.2852), exists (0.2780), derived (0.2677), interiors (0.2576), refers (0.2545)\n",
            "about   average (0.2804), ancestry (0.2733), mercator (0.2693), found (0.2690), diversity (0.2688)\n",
            "from    the (0.2945), timeline (0.2702), half (0.2522), tenth (0.2460), appended (0.2439)\n",
            "opened  park (0.3349), rail (0.3241), hotels (0.3217), buildings (0.3176), downtown (0.3172)\n",
            "lists   list (0.4232), web (0.3439), entries (0.3230), proverbs (0.3075), weblogs (0.3044)\n",
            "loss    losses (0.3766), shortages (0.3554), yield (0.3286), severe (0.3254), reduced (0.3221)\n",
            "dog     dogs (0.3880), greyhound (0.3441), appear (0.3225), cat (0.3208), spaniel (0.3187)\n",
            "volume  volumes (0.3918), published (0.3205), joule (0.3067), energy (0.3059), constant (0.3050)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 4/5: 100%|██████████| 8782/8782 [03:07<00:00, 46.81it/s, loss=1.11]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word       Most Similar Words\n",
            "---------  --------------------------------------------------------------------------------------------\n",
            "will       must (0.3905), if (0.3561), doesn (0.3535), that (0.3474), hence (0.3263)\n",
            "than       less (0.4089), rather (0.3339), more (0.3302), possible (0.3166), fewer (0.3163)\n",
            "after      years (0.3604), temporarily (0.3560), shortly (0.3452), was (0.3325), ended (0.3261)\n",
            "which      composed (0.3108), thus (0.2979), have (0.2838), to (0.2810), piece (0.2795)\n",
            "as         called (0.3045), encountered (0.2800), elements (0.2760), various (0.2731), since (0.2679)\n",
            "marked     occupying (0.2991), throughout (0.2869), highlight (0.2776), during (0.2710), ended (0.2697)\n",
            "brothers   brother (0.3081), grammy (0.3010), went (0.2840), stepson (0.2768), cameo (0.2765)\n",
            "extensive  rden (0.2776), most (0.2766), trolleybus (0.2724), barada (0.2700), inaccessible (0.2685)\n",
            "actions    human (0.3985), argued (0.3742), action (0.3417), coercion (0.3327), motivated (0.3327)\n",
            "gun        firearms (0.4823), firing (0.4475), weapon (0.4400), ammunition (0.4398), firearm (0.4301)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 5/5: 100%|██████████| 8782/8782 [03:08<00:00, 46.47it/s, loss=1.08]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word          Most Similar Words\n",
            "------------  --------------------------------------------------------------------------------------------\n",
            "s             browne (0.2683), loved (0.2648), endowed (0.2477), reynolds (0.2397), lifelong (0.2326)\n",
            "four          six (0.6214), two (0.6042), one (0.5994), three (0.5837), five (0.5747)\n",
            "these         are (0.3505), they (0.3115), have (0.3082), some (0.3049), exceptions (0.3003)\n",
            "some          those (0.3279), that (0.3101), these (0.3049), drawbacks (0.3040), memes (0.3027)\n",
            "see           disambiguation (0.3884), topics (0.3182), article (0.2972), external (0.2938), term (0.2867)\n",
            "understand    know (0.3814), discern (0.3418), objectively (0.3295), how (0.3288), what (0.3184)\n",
            "rich          fertile (0.2921), tomatoes (0.2876), abundant (0.2872), fresh (0.2790), kelp (0.2788)\n",
            "duke          earl (0.4305), dukes (0.3974), prince (0.3858), brabant (0.3832), margrave (0.3831)\n",
            "specifically  commonly (0.2537), are (0.2451), angered (0.2438), also (0.2355), specific (0.2346)\n",
            "numerous      throughout (0.3776), notably (0.3001), were (0.2741), thematic (0.2687), judean (0.2590)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "def plot_loss_history(loss_history, smoothing_window=50):\n",
        "    smoothed_loss = np.convolve(loss_history, np.ones(smoothing_window) / smoothing_window, mode='valid')\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.lineplot(x=range(len(loss_history)), y=loss_history, label='raw loss', color='blue', alpha=0.5)\n",
        "    sns.lineplot(x=range(len(smoothed_loss)), y=smoothed_loss, label='smoothed loss', color='orange')\n",
        "\n",
        "    plt.title(\"loss over training steps\")\n",
        "    plt.xlabel(\"training step\")\n",
        "    plt.ylabel(\"loss\")\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "plot_loss_history(loss_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "s35CyixLd9L_",
        "outputId": "99cd808d-2111-48e1-8474-664a658f4572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extract Trained Word Embeddings\n",
        "\n",
        "After training, input and output embeddings are extracted from the model. These embeddings represent words in a distributed format, and optionally, input and output embeddings can be averaged to create a unified representation for each word."
      ],
      "metadata": {
        "id": "9fJzUeBLrysc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "input_embeddings = model.input_embeddings.weight.data.cpu().numpy()\n",
        "output_embeddings = model.output_embeddings.weight.data.cpu().numpy()\n",
        "combined_embeddings = (input_embeddings + output_embeddings) / 2.0"
      ],
      "metadata": {
        "id": "vhBtbToF-dLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find Similar Words Using Cosine Similarity\n",
        "\n",
        "Given a query word, this section finds the most similar words based on cosine similarity in the embedding space. This functionality allows for an evaluation of the quality and semantic relationships captured by the embeddings."
      ],
      "metadata": {
        "id": "_BG9kPJisJVG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "similar_words = find_similar_words(\"computer\", embedding_matrix=combined_embeddings, top_count=10)\n",
        "print(tabulate(similar_words, headers=[\"Word\", \"Cosine Similarity\"], tablefmt=\"simple\", floatfmt=\".4f\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYR_PsW1RPVn",
        "outputId": "8f3e8da7-4bab-4b69-c10e-25fab39807fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word         Cosine Similarity\n",
            "---------  -------------------\n",
            "computers               0.6006\n",
            "software                0.4832\n",
            "hardware                0.4758\n",
            "computing               0.4694\n",
            "machines                0.4566\n",
            "emulator                0.4486\n",
            "pdp                     0.4430\n",
            "desktop                 0.4250\n",
            "cpu                     0.4218\n",
            "console                 0.4202\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize Word Embeddings with t-SNE\n",
        "\n",
        "Using t-SNE, word embeddings are reduced to two dimensions for visualization. Words are color-coded by predefined categories to highlight how well the embeddings cluster semantically similar tokens, providing insights into the model’s performance."
      ],
      "metadata": {
        "id": "WE8SclVssVv-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "\n",
        "def plot_colored_embeddings_with_tsne(\n",
        "    word_categories,\n",
        "    embedding_matrix,\n",
        "    category_colors,\n",
        "    word_to_index,\n",
        "    perplexity=30,\n",
        "    text_offset=0.02\n",
        "):\n",
        "    word_to_category = {\n",
        "        word: category\n",
        "        for category, words in word_categories.items()\n",
        "        for word in words\n",
        "    }\n",
        "\n",
        "    all_words = list(word_to_category.keys())\n",
        "    valid_words = [word for word in all_words if word in word_to_index]\n",
        "    word_indices = [word_to_index[word] for word in valid_words]\n",
        "    vectors = embedding_matrix[word_indices]\n",
        "\n",
        "    tsne = TSNE(n_components=2, random_state=42, perplexity=perplexity)\n",
        "    reduced_vectors = tsne.fit_transform(vectors)\n",
        "\n",
        "\n",
        "    plt.figure(figsize=(10, 10), dpi=150)\n",
        "    for i, word in enumerate(valid_words):\n",
        "        category = word_to_category[word]\n",
        "        color = category_colors.get(category, \"black\")\n",
        "\n",
        "        x, y = reduced_vectors[i]\n",
        "        plt.scatter(x, y, color=color, alpha=0.8, edgecolors='k', linewidths=0.5)\n",
        "\n",
        "        plt.text(\n",
        "            x, y + text_offset, word,\n",
        "            fontsize=10,\n",
        "            color=\"black\",\n",
        "            ha=\"center\",\n",
        "            va=\"bottom\",\n",
        "            bbox=dict(\n",
        "                boxstyle=\"round,pad=0.2\",\n",
        "                ec=\"none\",\n",
        "                fc=\"white\",\n",
        "                alpha=0.7\n",
        "            )\n",
        "        )\n",
        "\n",
        "    plt.title(\"word embeddings visualization\", fontsize=16, pad=20)\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "jhlfZDEqS5Q8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_categories = {\n",
        "    # Existing Categories\n",
        "    \"sports\": [\"soccer\", \"football\", \"baseball\", \"basketball\", \"tennis\"],\n",
        "    \"countries\": [\"america\", \"canada\", \"france\", \"germany\", \"italy\", \"japan\", \"china\", \"india\", \"russia\"],\n",
        "    \"cities\": [\"paris\", \"berlin\", \"rome\", \"london\", \"tokyo\", \"moscow\"],\n",
        "    \"animals\": [\"dog\", \"cat\", \"horse\", \"elephant\", \"lion\", \"tiger\", \"zebra\"],\n",
        "    \"foods\": [\"pizza\", \"pasta\", \"burger\", \"fries\", \"coffee\", \"tea\", \"wine\"],\n",
        "    \"music\": [\"rock\", \"jazz\", \"classical\", \"guitar\", \"piano\"],\n",
        "    \"literature\": [\"poetry\", \"novel\", \"author\", \"drama\", \"fiction\"],\n",
        "    \"mythology\": [\"zeus\", \"apollo\", \"thor\", \"odin\", \"hera\"],\n",
        "    \"planets\": [\"mars\", \"venus\", \"jupiter\", \"saturn\", \"mercury\"]\n",
        "}\n",
        "\n",
        "category_colors = {\n",
        "    \"sports\":   \"#e41a1c\",\n",
        "    \"countries\": \"#377eb8\",\n",
        "    \"cities\":   \"#4daf4a\",\n",
        "    \"animals\":  \"#984ea3\",\n",
        "    \"foods\":    \"#ff7f00\",\n",
        "    \"music\": \"#f781bf\",\n",
        "    \"literature\": \"#a65628\",\n",
        "    \"mythology\": \"#ffff33\",\n",
        "    \"planets\": \"#bcbd22\"\n",
        "}\n",
        "\n",
        "plot_colored_embeddings_with_tsne(\n",
        "    word_categories=word_categories,\n",
        "    embedding_matrix=combined_embeddings,\n",
        "    category_colors=category_colors,\n",
        "    word_to_index=word_to_index,\n",
        "    perplexity=30,\n",
        "    text_offset=0.02\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xOx7PjxBfoL5",
        "outputId": "84d00d15-2f71-4904-e845-9cc83fdf4e3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": 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FihXTF198keUN00wmk7766is5OTlJko4eParVq1fneDxvv/22SpUqlW3M9OnT7Y+7du2qFi1aZBn7yCOPqGfPnjnu92Z8+umn9seffPJJjvn2799f1atXlyStXLkyx5vDPfjgg/Z2K5m58Xg6duyoli1b5io+tV1LTnr37p2h9UFarVq10lNPPWV/Pm3atAwxixcv1uHDhyVJnTp1UufOnbPdZ7ly5TR8+HBJ128K+PPPP6dbbxiGvvvuO/vzt99+W2XKlMlyvJEjR8rf3z/bfUqF/35KVbp0ab399ttZri9btqzatm1rf57Za3np0iUtWLDA/vzjjz/OtlXJ+++/n2OLiitXrqTLsSD06dPH/vinn37K9saHs2bNsj/u1atXrm/imFZhHOPt4OTkpN69e0uSrl69qj/++KPQcjEMQ3379tWePXskSb6+vlq4cOEttc5Ja8CAAfbHv//+e57Gulk//fST/UakZcuW1dixY7OMdXZ21pdffml/vm7duly19vnss89kNmfeadVkMqU7/tz+fw4AANKjaA4AQBFQt25dex/z3bt32/sep0rtUe7v769KlSrZi+GhoaH6999/M42VMi+ar1271v74ySefVLly5bLNrWLFinr88cftzzMr1N8ouz7NmY3Tr1+/HOOffvrpHGNyKzEx0V789/T0VLt27XK1XWoh1jCMdH3QM9O1a9ds11euXFkWiyXX8akflEiyf+iRk5s9r5m9tsuWLbM/7tWrV672m7b4f2PB7++//9bZs2clSWazOccxnZyccrXfwnw/pdW+ffscC4q1a9e2Pw4PD8+wfsuWLbLZbJKufwjx6KOPZjueh4eHOnbsmG1M2n71v/76q86fP59tfH549NFHVb58eUnSyZMn0/XDTstqtWrhwoX252mL7TejMI7xVkVHR2vFihX65JNPNHr0aA0bNkwvvvii/WvVqlX22NSCdWEYM2aM/QMci8WiRYsW5fgzQ7r+gdkff/yhr776Sm+99ZaGDx+e7vjS9iov6ONL+zOwZ8+ecnV1zTb+oYce0gMPPGB/ntPPwMqVK6tOnTrZxuT0fwAAAMgZNwIFAKAIMJvNaty4sVasWKGkpCRt3LhR7du3lyTFx8fbZ6KlFm1btGihSZMmyTAMbdiwwT7799q1a9q2bZuk6zPkGjVqlGFfu3fvtj/ObhZyWo0bN9bixYslSbt27co2tlKlSipRokS2MWfOnNGFCxfszxs2bJhjDg0bNpTJZMp2tmpu7d27V1arVdL1omxubu4mSX/99Zf98alTp7KNTVvkzoqXl5c9j+Dg4Gxj057TtDNqs2IymdSgQYMc49K+R86dO6fIyEh7oVOStm7dan88f/58bdiwIccx037oc+N5Svv+q169eqY3ss0ux8wU9vsprbTFtaykvbFjZq9l2iJi/fr15eCQ8zyaBg0a6Mcff8xyfcOGDeXr66tTp07p5MmTCg4O1oABA9S+fXs1aNBAxYoVy3EfN8vBwUE9e/a0X9Uxa9YsNW3aNEPcwoUL7d8HtWvXzvF7ISuFcYw36/Tp0xo1apTmzZtnvyloTi5evHibs8rcrFmz7DeYNZlMmjVrlmrVqpXtNvHx8frggw80ZcqUXOdd0Md3qz8D9+3bJynnn4H58X8AAADIGUVzAACKiObNm2vFihWSrs8WTy2ab9261V5cSZ053qxZM3vBb/369fai+Z9//qn4+HhJ12fHZTaDLm1xMTdtLyQpICDA/jinAkdu2iKkzcHNzS3H1ijS9RnhxYsXV3R0dI6xOYmIiLA/joqKSjfrMbcuXbqU7frixYvnOEbay/dzik8bm5iYmOPY3t7e9qsXslO6dGm5uLjo6tWrkq6/NmmL5mnP1dy5c3Mc70Y3nqe0r72fn1+uxsgprrDfT2nl5nVPbXckXZ+Re6O0x5N29nR2fHx8ctznjz/+qHbt2ik2NlYXL17Uxx9/bG/9Uq9ePTVt2lRPPvmkHn744Vtqj5KZPn362Ivm8+bN05dffpmheD179ux08beqsI4xt3bv3q1HH300x/87bhQTE3ObMsratm3bNGjQIPvzDz74QJ06dcp2m0uXLqlly5Y3PXO8oI/vdv8MzI//AwAAQM5ozwIAQBGRtgdz2su/07ZbSY0pUaKEfTZbTrE3Su3lKilde5DspI3LqcCR06XuN+bg5uaWqxxuzCMvbmx/cytyKlzfbEEuvwt4t3peb3x983qubjxPt/La5/S6F/b7Ka38eB1v5Xjc3d1zjGnWrJlCQ0PVr1+/dN+nqX2zP/jgAzVp0kTVq1dP1y4lL9LOHL906ZKWLl2abv358+ftrZIcHR1z3QIoK4VxjLlx7do1denSxV4wL126tN58802tW7dOp06dktVqVXJysgzDkGEY+v777+3bJicnF1ie0vVWOp06dbJ/WNuvXz+NGjUqx+2GDh1qL5gXK1ZMgwYN0m+//aawsDDFxMQoMTHRfnxpW0wV9PHd7p+BBf1hDAAARRVFcwAAioi0fc1DQ0PtxZXUonjlypXTzbhNLYrv37/fPvMtp5uASumLa6ktEXKSNi43s5dzkjaHuLi4XG+X23xzkrYAUrNmTXsh52a+3n333XzJ5Xa51fN64+ub9lzt2rXrps/Tjf16b+W1z+l1L+z3U367HecoVeXKlTVz5kxduHBBK1as0JtvvqkWLVqkKzCHhYWpc+fO6W6WmxdpZ4+nnVUuXb96IfWDlUcffTRX/bJzUhjHmFPhd/78+fZCccWKFRUaGqr33ntPzZs3l4+Pj9zc3NIVWwtjdrl0/X3UoUMHnTt3TtL1tiTffPNNjtudOXNGc+bMkXS9Lc+KFSs0bdo0dejQQffdd5/c3d3l6Ohojy+s45PujJ+BAAAg7yiaAwBQRDg6OqpJkyaSrhdgNmzYkGk/81SpRfHUFi256WcupW+fcvLkyVzllrbwmZvWFzlJm0NcXJyioqJy3CYmJiZfZohLUtmyZe2PU29Kea+5dOlSuhmVWbl48aK9NYuU8fXN73N1K++/nPrHF/b7Kb+lfQ1Onz6dq21yG5fKYrHoscce03vvvae1a9cqKipKv/zyS7p+zG+88YbOnDlzU+Nmpnfv3vaC8JIlS9Kd91mzZtkf9+3bN8/7Sisvx3iz7ZByei+tWbPG/nj48OHpWiBl5sSJEznuM78ZhqE+ffooNDRU0vXWJb/++qucnZ1z3Hbt2rX2+wM88cQTWV7plKowji/VnfAzEAAA5B1FcwAAipC0s8PXr1+vLVu2yGazZVgnXW9DkHqDwPXr12vbtm324meDBg3k4uKS6T5q165tf7xly5Zc5ZU2rk6dOrnaJjsVK1ZMV7hILfZnZ9u2bfl208ZatWrZC0Hnz5/XkSNH8mXcO4lhGPrzzz9zjEt7o8+yZcuqQoUK6danvZno5s2b85xX2vffP//8k6vCddocM1PY76f8lvZmi3/99Veu8kz9cO1Wubq6qmvXrlq/fr39gxKbzaaVK1fmaVzpel/21BuAXrt2TfPmzZMkHTlyxJ63xWKx35vhdrmZY/T09LQ/zulDGJvNprCwsGxj0t4bIDc3ity4cWOOMflt9OjR9pY1Hh4eWrx4scqUKZOrbe+G40t1J/wMBAAAeUfRHACAIiRtYXzdunXZ9ij39vZWzZo1M43NqjWLJLVs2dL+eNmyZTp//ny2OUVERGj58uWZbp8XaY/nxx9/zDH+hx9+yJf9SteLZ2mP4+uvv863se8kN3teM5sd2q5dO/vj7777Lt2s9FtRvXp1ewuOxMREhYSEZBufmxipcN9P+e3hhx+23ywzMjJSa9euzTY+NjY23/pzlyhRQo0bN7Y/T23TkVdpW7Skzi5PO8u8U6dOt6XHfGZyc4xpb/yY040tFy1alOP3ReoHnFLOLXd27typv/76K9uY/Pbjjz/qww8/lHQ915CQkFwVv1PdzPHFxcXl+vsv7Ye/+XXDzLT/98+ZMyfH127Hjh3au3ev/XlOs+gBAEDBoGgOAEARUrduXfsMx3379unXX3+VJN13332qWLFihvjUP94PHjyoX375JcPyzLRp00aVKlWSdH3W5/Dhw7OMNQxDL730kr1YUaVKFbVq1ermDioLgwYNsj/++eefs515uHnzZv3000/5st9Ur7/+uv3xpEmT9Pvvv+d627ulpcusWbOynW2+bt06zZ8/3/487WuSqkuXLqpataqk6wXcF154IdcztGNjYzP0DHZwcNDAgQPtz//3v//pwoULWY4xYcKEdDcNzEphv5/yU4kSJdSxY0f785EjR9pvypiZt99+O8cZ+7lpWZMqbTuc3M40zkm3bt3sBdCNGzfq9OnT6fqb50drlvw8xrRXWMyYMSPLca5cuZKrm2RWrlzZ/njRokVZxsXFxenZZ5/Ncbz8tGXLFg0ePNj+/KOPPlLbtm1vaoy0x7ds2TIlJSVlGfvqq6/m+sOYkiVL2h/nR6sgSerVq5e9r3lkZKT+97//ZRlrs9n00ksv2Z+3aNFC1apVy5c8AABA3lA0BwCgCEnb19wwDB08eFBS1jPH0y4/cOCApOv9zBs2bJjlPhwcHOwzCiUpJCREgwcPztD/OiYmRgMGDLAX7qXrxZS0MwrzonXr1vbivmEY6tSpk5YtW5YhbtWqVerQoYOSk5Pl5OSUL/uWrre3efrppyVdn83ctm1bjRs3Lss+4FevXtXChQvVsWNHdejQId/yuF2cnJyUlJSkdu3aZfqBwNKlS9W5c2d7Abx169Z69NFHM8Q5Ojpq8uTJ9pv4ff/992rbtq3+/vvvLPe9Z88evf766/L19c204P3f//7X3hf47Nmzat26tf755590McnJyfrkk080ZswY+6zr7BT2+ym/vfPOO/bj3rVrlzp27Jih0JiQkKC33npLEydOzLHv9KRJk1SrVi1Nnjw5yw99YmNjNWbMGPssZ0dHR7Vp0yYfjkYqXry4/aqF5ORkDR8+3N4WqVy5cvnyYVx+HmO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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Learn More in the VLMs Zero-to-Hero Course\n",
        "\n",
        "This notebook is part of the [VLMs Zero-to-Hero](https://github.com/SkalskiP/vlms-zero-to-hero) series, which covers everything from foundational concepts like Word2Vec to advanced vision-language models. If you're interested in diving deeper into NLP, computer vision, and their intersection, check out the rest of the course for more hands-on examples and practical insights.\n"
      ],
      "metadata": {
        "id": "3Za1UAE-xOBB"
      }
    }
  ]
}